Cultural Humility in Simulation Education: A State of the Science
Bibliographic record
Abstract
American Association of Colleges of Nursing. (2008). Cultural competency in Baccalaureate nursing education. Washington DC: Author. Foronda, C., Baptiste, D., Ousman, K., & Reinholdt, M. (2015). Cultural humility: A concept analysis. Journal of Transcultural Nursing, 1-8. doi: 10.1177/1043659615592677 Graham, C. L., & Atz, T. (2015). Baccalaureate minority nursing students’ perception of high-fidelity simulation. Clinical Simulation in Nursing, 11(11), p. 482-488. http://dx.doi.org/10.1016/j.ecns.2015.10.003 Grossman S. Nursing students identify fears regarding working with diverse critically ill patients: Development of guidelines for caring for diverse critically ill older adults. Dimens Crit Care Nurs. 2013;32(5):237-243. Institute of Medicine. (2010). The Future of Nursing: Leading change, advancing health. The National Academies Press, Washington D.C. International Nursing Association for Clinical Simulation and Learning. (2015) Standards of best practice: Simulation SM. Retrieved from http://www.inacsl.org/i4a/pages/index.cfm?pageID=3407 Jeffery, C., A., Mitchell, M., L., Henderson, A., Lenthall, S., Knight, S., Glover, P., . . . Groves, M. (2014). The value of best-practice guidelines for OSCEs in a postgraduate program in an Australian remote area setting... objective structured clinical examinations. Rural & Remote Health, 14(3), 1-9. Kamau-Small, S., Joyce, B., Bermingham, N., Roberts, J., & Robbins, C. (2015). The impact of the care equity project with Community/Public health nursing students. Public Health Nursing, 32(2), 169-176. doi:10.1111/phn.12131 Kutob, R., M., Bormanis, J., Crago, M., Gordon, P., & Shisslak, C., M. (2012). Using standardized patients to teach cross-cultural communication skills. Medical Teacher, 34(7), 594-594. doi:10.3109/0142159X.2012.675101 Leake, R., Holt, K., Potter, C., & Ortega, D. M. (2010). Using simulation training to improve culturally responsive child welfare practice. 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Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".